Internship Programs
The Internship Program in AI and DS at AIDE is designed to bridge the gap between academic learning and industry requirements by providing students with practical experience in applying AI and DS concepts to real-world problems. Interns have the opportunity to work on industry-sponsored projects, research initiatives, and collaborations with faculty members and industry partners.
Key Features
- Industry Projects: Interns work on industry-sponsored projects under the guidance of experienced mentors and industry professionals. These projects cover a wide range of AI and DS applications, including machine learning, deep learning, natural language processing, computer vision, big data analytics, and more.
- Hands-on Experience: The program emphasizes hands-on learning, allowing interns to apply theoretical concepts and methodologies to practical projects. Interns gain experience working with datasets, building models, developing algorithms, and implementing AI and DS solutions.
- Certificate of Completion: Upon successful completion of the internship program, interns receive a certificate of completion from AIDE, recognizing their contributions, achievements, and skills acquired during the internship.
More Information
Internships
- All Positions
- Project Staff
- Post Doc
- Internships
- Design Credits
All Backgrounds
- All Backgrounds
- AIML
- Computational
- Electronics
- Physics
- Quantitative
- Software
Slot machines in our pockets: Elucidating the neural effects of smartphone overuse on reward anticipation and reward processing
Start Date: 01-02-2025
End Date:
Position Type: Post Doc
Smartphone app development for tracking user interactions and physiological data.
Background: Computational,Electronics,AIML,Software
Development of an oscillator network model of the brain that will enable hypothesis-driven perturbation-response experiments for early detection of neurodegenerative disorders
Start Date: 01-08-2024
End Date: 15-11-2024
Position Type: Design Credits
The student will use an existing whole-brain network model of a mouse brain, and study how alterations in its connectivity parameters changes the likelihood of epileptic seizures.
Background: Quantitative,Computational,Physics